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All Journal Insyst : Journal of Intelligent System and Computation JRTI (Jurnal Riset Tindakan Indonesia)
Asriwan Guci
Universitas MERCUBAKTIJAYA Padang
Religion Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Social Sciences Other

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An Explainable Hierarchical Rule-Based Model for Inventory Optimization in Data-Scarce Culinary MSMEs Yaslinda Lizar; Asriwan Guci
Intelligent System and Computation Vol 8 No 1 (2026): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v8i1.480

Abstract

Inventory management is a critical component of supply chain operations, particularly for culinary Micro, Small, and Medium Enterprises (MSMEs) that face volatile demand patterns and perishable raw materials. Inaccurate procurement decisions may result in overstocking, stock shortages, and financial losses. Although artificial intelligence approaches such as machine learning have been widely applied in inventory forecasting, these methods typically require large-scale historical datasets and advanced computational infrastructure, which are often unavailable in MSME environments. This study proposes an explainable hierarchical rule-based inference model designed to support inventory optimization in data-scarce culinary MSMEs. The model integrates dynamic operational factors, including day type, weather conditions, supplier lead time, and special events, into a transparent decision-making mechanism based on IF–THEN rules. The research adopts a Research and Development methodology using the Waterfall framework, covering requirement analysis, rule-base construction, system implementation, and evaluation. Model validation was conducted through 20 expert-verified decision scenarios and assessed using confusion matrix metrics. The experimental results demonstrate that the proposed system achieved 90% accuracy, 92.3% precision, 92.3% recall, and a 92.3% F1-score when compared with expert procurement decisions. These findings indicate that explainable rule-based systems remain a practical and reliable solution for inventory decision support in culinary MSMEs with limited data resources.
Development and validation of an explainable rule-based clinical decision support system for childhood immunization using rule traceability Yaslinda Lizar; Asriwan Guci; Dony Novaliendry
JRTI (Jurnal Riset Tindakan Indonesia) Vol. 11 No. 1 (2026): JRTI (Jurnal Riset Tindakan Indonesia)
Publisher : IICET (Indonesian Institute for Counseling, Education and Therapy)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/30036862000

Abstract

Childhood immunization is essential for preventing vaccine-preventable diseases, yet immunization decisions in primary healthcare are often affected by false contraindications, inconsistent guideline interpretation, and dynamic clinical conditions. This study developed and evaluated an Explainable Clinical Decision Support System (X-CDSS) that integrates rule-based reasoning, dynamic clinical variables, and explainable rule traceability to provide transparent, guideline-compliant immunization recommendations. A Research and Development (R&D) approach with iterative prototyping was employed. Knowledge from the Indonesian national immunization guideline, scientific literature, field observations, and healthcare professionals was formalized into 35 IF–THEN production rules implemented using a forward-chaining inference mechanism in a web-based application. The system was evaluated through functional testing, clinical validation using 40 representative scenarios, performance evaluation, and User Acceptance Testing involving seven healthcare professionals. The proposed system achieved an accuracy of 92.5%, precision of 96.6%, recall of 93.3%, an F1-score of 94.9%, and a Cohen's Kappa coefficient of 0.81, with an overall usability score of 4.3/5.0. These findings demonstrate that the proposed X-CDSS provides reliable, transparent, and clinically accountable decision support for routine childhood immunization in primary healthcare.
Co-Authors Dony Novaliendry Yaslinda Lizar Yaslinda Lizar